Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5518
Title: A mixture of experts network structure for EEG signals classification
Authors: Güler, İnan
Übeyli, Elif Derya
Güler, N. F.
Keywords: Discrete wavelet transform
Eeg signals classification
Expectation-maximization algorithm
Mixture of experts
Issue Date: 2005
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, 1 September 2005 through 4 September 2005, Shanghai, 69123
Abstract: This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for classification of electroencephalogram (EEG) signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structure was implemented for classification of the EEG signals using the statistical features as inputs. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 93.17% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2005 IEEE.
URI: https://doi.org/10.1109/iembs.2005.1617029
https://hdl.handle.net/20.500.11851/5518
ISBN: 0780387406; 9780780387409
ISSN: 0589-1019
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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